AI for Personal Injury Lawyers: Medical Chronologies, Demand Letters and the HIPAA Line

The prompt that cost Morgan & Morgan its Wyoming motion was one line long. In January 2025 a lawyer at the No. 42 US law firm by headcount uploaded a draft motion in limine to MX2.law, the firm’s in-house AI platform, and asked it to “add to this Motion in Limine Federal Case law from Wyoming”. Eight of the nine cases it added did not exist, two partners e-signed without reading, and Judge Kelly Rankin’s sanctions order followed a month later.

That is the shape of AI for personal injury lawyers in 2026: the plaintiff-side practice with the highest generative-AI adoption, the most repetitive document work, the most sensitive data in civil practice, and one of the most quoted sanctions orders. The tools work; the failure modes are specific and avoidable. Here is the workflow for chronologies and demands, the HIPAA rule that decides which tool you may use, and a five-rule policy.

The numbers: 37% adoption and where the hours go

CasePeer, citing 8am’s personal injury survey, puts generative-AI use among PI lawyers at 37% against 31% across all practice areas; the uses are prosaic: correspondence (52%), brainstorming (46%) and document drafting (39%).

The hours go on medical records. EvenUp’s guide estimates manual review at “10 to 20 hours” per moderately complex case; its three-stage pipeline (OCR and extraction, chronology, then categorisation with duplicate flagging and narrative summaries) is what every PI-specific tool now sells.

Medical chronologies: OCR first, extract second, judge last

The unglamorous step decides the quality. A litigator on r/legaltech: “I pre-process everything with basic OCR cleanup before it hits any AI tool otherwise you get hallucinated dates, mangled drug names, and unusable citations” (/u/Specific_Citron_8546). Scanned records with handwritten margins are where a model fills gaps with plausible text.

Then extract into columns with a page reference for every cell, so the paralegal checks rows instead of re-reading the file. The general method is in the case chronology guide; the PI version adds gaps, pre-existing conditions and a specials total.

From the attached medical records (OCR-cleaned; PHI handled in our BAA-covered tool), build a chronology with exactly these columns: Date | Provider | Encounter type | Complaint or diagnosis (quoted) | Treatment | Work restrictions | Billed amount | Page.
Then list: (1) treatment gaps over 30 days, with the page before and after each gap; (2) every pre-existing condition or prior injury mentioned, with the page; (3) inconsistencies between providers on mechanism of injury or symptoms; (4) a running total of special damages, which I will re-add.
Write NOT IN RECORDS rather than inferring anything. Do not estimate general damages or settlement value.

Check the gaps against the records before anyone writes “gap in treatment” in a demand: a missing page looks identical to a missing month. And re-add the total; Thomson Reuters’ own prompting guide lists “math, counting, and sorting” among the things these models do badly.

Demand letters: six steps, five prompts, one rule

CasePeer’s six-step workflow refuses to let the model write the letter in one go: choose a tool that lets you upload and export; gather the complete file; instruct on structure, tone and emphasis; generate an outline and confirm it; build each section separately (case summary, liability, damages); review every figure and date. Its published prompts map onto the sections.

The one rule is CasePeer’s caveat: “If critical facts are missing or misunderstood, the resulting draft may contain legal inaccuracies.” Given “$18,000 in medical bills” and nothing else, a model will supply the ER visit and the MRI that would explain the bill. So interview before drafting. And since AI struggles, in CasePeer’s words, to convey “pain, trauma, or loss in a way that resonates with adjusters”, the paragraph about your client’s inability to lift her grandchild is yours to write.

I am preparing a demand letter to [insurer] on a [motor vehicle / premises] claim under [state] law. Do not draft yet. Ask me, in one message, the ten questions whose answers most change the letter: liability facts and the police report's findings, injuries and diagnoses, treatment dates and gaps, specials by category, lost income and its proof, pre-existing conditions, known policy limits, and the outcome the client wants. Then list the documents you need before writing the liability, treatment and damages sections, one section per prompt.

The sections are then built from the chronology you verified, with the file’s figures pasted in rather than remembered. That sequence, section by section with verification between steps, is the one we build on anonymised material in AI Lab for Lawyers; it is the only one that survives an adjuster who reads carefully.

Screening and intake: “far less horrifying”

A lawyer doing criminal defence and PI gave r/Lawyertalk the best description of where the value sits: “Where it really shines is finding me my needle in the haystack of a large file. It summarizes medical records pretty well too, and makes doing med mal screenings and intakes far less horrifying, so i feel comfortable taking punts on more consults” (/u/Mean_Economist6323).

The screening prompt is the chronology prompt with one change: every ordered, delayed or cancelled test with its page, and no opinion on breach or causation.

HIPAA: which tiers carry a business associate agreement

Medical records are protected health information; a tool that processes them for you needs a business associate agreement (BAA). Supio’s summary of OpenAI’s position is that the standard ChatGPT tiers (Free, Plus, Team, Business) are not HIPAA-compliant and carry no BAA; only Enterprise and Edu accounts, sales-managed and with a signed BAA, qualify. OpenAI’s own pages could not be re-checked as of September 2026, so confirm current terms in writing before any record goes in.

The Business vs Enterprise guide explains why a “no training” promise is not a BAA. Protective orders now bite as well: Morgan v. V2X (D. Colo., 30 March 2026) bars AI platforms unless the provider is contractually barred from training on inputs and disclosing them, and Jeffries v. Harcros (D. Kan., 25 March 2026) banned public AI for all discovery material (Akin’s summary).

The adjuster’s inbox: volume, and the number the chatbot gave your client

Two counter-currents run against automated demands. First, volume: insurers are reported to complain that plaintiff attorneys are using AI tools “to generate large volumes of demand packages” (Risk & Insurance), and a package that reads like one of a thousand gets the attention due to one of a thousand. The second is the number already in your client’s head. The same r/Lawyertalk lawyer: “AI substantially over values cases if you ask it about what a reasonable settlement should be … clients cant help but ask chat or claude what their case is worth.”

So do not ask the model for a value. Ask for the structure and supply the numbers yourself.

Structure a settlement memo for a [rear-end collision] claim in [state]. Sections: liability strengths and weaknesses from <facts> only; each head of damages with the evidence we hold and what is missing; litigation cost to trial from <budget>; procedural risks; the carrier's likely reservation point and why; a decision tree with placeholders [P1], [P2] for probabilities I will supply; and the questions the client will ask. Do NOT estimate probabilities, verdict ranges or settlement values. Write for a client who will read it in ten minutes.

When the client arrives with a figure from ChatGPT, the memo explains why yours is different; the client communication guide covers that conversation, and the point that after United States v. Heppner (S.D.N.Y., February 2026), where a defendant’s own Claude conversations were held not to be privileged, the client’s chatbot valuation may be discoverable.

Wadsworth v. Walmart: the in-house tool that invented eight cases

Two lessons survive: a firm-built tool hallucinates like any other, and a prompt that asks the model to supply authority rather than work with authority you supply is the most dangerous prompt there is. Judge Rankin’s line, “The instant case is simply the latest reminder to not blindly rely on AI platforms’ citations regardless of profession”, applies to the partner who signs as much as to the associate who prompts.

The demographics fit: Stanford’s analysis of the hallucination database found the plaintiff side accounts for 56% of US lawyer cases. The sanctions timeline has the rest, and the citation verification guide the six-layer check that would have caught all eight.

AI for personal injury lawyers: a five-rule policy

Morgan & Morgan’s first response was a firm-wide email; Butler Snow had a written policy for two years before three partners were disqualified in Johnson v. Dunn. A policy is not a control, as the policy template explains, but for a PI practice the rules fit on one page.

1. PHI only in BAA-covered tools. Consumer ChatGPT, Claude, Gemini and Copilot never see a medical record.
2. OCR and clean before upload. Someone spot-checks digits, dates and drug names.
3. Extract with page references; verify every cell you rely on before it appears in a demand.
4. No authority from the model. Case law comes from a database and is read by the signer; the Ninth Circuit’s standard in Lnu v. Blanche (June 2026) is that a competent and diligent attorney “must also read and reason”.
5. Nobody asks it for a value. Settlement ranges and general damages are the lawyer’s, and the client is told why the chatbot’s number is wrong.

The criminal defence guide has the body-cam version; the other practice-area guides have the rest.

Where to go next: the practice-area hub maps every specialism’s tasks and risks, and the prompt library holds the full PI recipe from records to demand. In AI Lab for Lawyers, PI lawyers build the chronology prompt on anonymised records and check, tool by tool, where HIPAA stops.